研一学生关庆澍的文章被机器人顶会 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021) 接收。本届IROS会议共收到2801投稿，最终录稿率为45%。

本文由西安交通大学和哈尔滨工业大学联合提出，关注了机器人路径规划问题，解决了传统SOM路径规划存在的闭环初始化和路径交叉问题，有效提升了路径规划的效率。

中文题目： 重温基于Kohonen自组织映射网的路径规划

Title: Kohonen Self-Organizing Map based Route Planning: A Revisit

Authors: Qingshu GUAN, Xiaopeng Hong, Wei Ke, Liangfei Zhang, Guanghui Sun, Yihong Gong

Abstract:

In this paper, we revisit the long-standing Traveling Salesman Problem (TSP) and focus on the challenging, yet practical route planning problem with limited computational resources. We make contributions to TSP, one of the most famous NP-hard problems by providing a new improved approximate solution, which we term TOpology Preserving Self-Organizing Map (TOPSOM). TOPSOM well preserves the topology of the node map to be traversed by maintaining the continuity of nodes and the distances between them. In addition, to satisfy the requirements of convex hull, we design an elastic competitive Hebbian learning rule. TOPSOM can solve large-scale TSPs with high precision and high efficiency with limited computational costs. Extensive experimental results on mainstream route planning benchmarks including TSPLIB and National TSP's show that our method consistently outperforms baseline methods, by up to 7.7\% in terms of the Percent Deviation of Mean solution to best-known solution.